Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Jun 25, 2026)
Date Submitted: Jan 7, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
“Development and Usability Evaluation of an AI Vision-Based Self-Management and Home Rehabilitation APP in Breast Cancer Patients On Endocrine Therapy”
ABSTRACT
Background:
Endocrine therapy is an essential component of breast cancer care but is often associated with musculoskeletal symptoms and reduced long-term adherence. Mobile health (mHealth) apps can provide scalable support for home-based exercise rehabilitation; however, evidence on the usability of artificial intelligence (AI) - driven apps in this context remains limited.
Objective:
This study aimed to develop a vision-based home rehabilitation app (SPARK) grounded in Symptom Management Theory (SMT) for breast cancer patients undergoing endocrine therapy, and to evaluate its usability. SPARK integrates AI-driven motion recognition and interactive feedback to support personalized symptom management.
Methods:
A multidisciplinary team designed the app based on the SMT and incorporated AI visual algorithms for movement recognition and feedback. Twenty-four patients participated in a 4-week app-based intervention. Usability was assessed using the Post-Study System Usability Questionnaire (PSSUQ), the User Interaction Satisfaction Questionnaire (QUIS), and post-intervention qualitative interviews. Quantitative data were analyzed descriptively, and qualitative data were thematically analyzed.
Results:
The app comprised five modules: symptom logging, movement capture, AI-based motion computation, real-time personalized feedback, and encrypted data management, enabling symptom monitoring and rehabilitation evaluation. Usability testing showed high levels of satisfaction across dimensions of usefulness, information quality, and interface design (PSSUQ Cronbach’s α=0.978; QUIS Cronbach’s α=0.965). Qualitative feedback indicated that patients found the app convenient and motivating, particularly for exercise adherence, while suggesting improvements such as enhanced interactivity and personalized rehabilitation plans.
Conclusions:
The AI vision–based self- management and home rehabilitation app demonstrated favorable usability and strong potential to support personalized, home-based exercise interventions for breast cancer patients on endocrine therapy. These findings highlight the feasibility of integrating AI-driven digital tools into long-term cancer survivorship care and provide a foundation for larger-scale trials. Clinical Trial: none
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